OpenRaiser/Intern-Atlas huggingface dataset:
BlockLoc: Secure Localization in the Internet-of-Things using Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

Several IoT applications are tightly dependent on the locations of the devices. However, localization algorithms can be easily compromised by injecting false locations. In this paper, we propose a Blockchain-based secure localization algorithm for the Internet of Things (IoT). The algorithm uses a public ledger (Blockchain) that contains nodes position and the list of their neighbor nodes. This ledger is shared among the IoT devices. Once an IoT device is localized its new position and the list of neighbor nodes are added to the Blockchain. This shared localization data will be used later by other IoT devices for their localization process. To avoid the attack where a malicious node sends a fake position, the correctness of the claimed position are verified before adding it to the Blockchain. Moreover, data exchanged between nodes (IoT devices) are signed to guarantee their authenticity and integrity. The integration of these security mechanisms into the localization process permits to exclude false data and therefore reduces the localization error. The simulation results show that adding the proposed security mechanism improves the localization accuracy of the algorithm when running in the presence of malicious nodes.
A Note on Computational Complexity of Dou Shou Qi
Venue: arXiv cs.CC 2019
Year: 2019

Dou Shou Qi is a Chinese strategy board game for two players. We use a EXPTIME-hardness framework to analyse computational complexity of the game. We construct all gadgets of the hardness framework. In conclusion, we prove that Dou Shou Qi is EXPTIME-complete.
Eigen Values Features for the Classification of Brain Signals corresponding to 2D and 3D Educational Contents
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect answers for both 2D and 3D questions. Using the classification technique, the impacts of 2D and 3D multimedia educational contents on learning, memory retention and recall will be compared. The subjects learn similar 2D and 3D educational contents. Afterwards, subjects are asked 20 multiple-choice questions (MCQs) associated with the contents after thirty minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers. Excellent accuracies obtained by both classifiers and by applying statistical analysis on the results, no significant difference is indicated between 2D and 3D multimedia educational contents on learning, memory retention and recall in both STM and LTM.
Different Stages of Wearable Health Tracking Adoption & Abandonment: A Survey Study and Analysis
Venue: arXiv cs.CY 2019
Year: 2019

Health trackers are widely adopted to support users with daily health and wellness tracking. They can help increase steps taken, enhance sleeping pattern, improve healthy diet, and promote overall health. Despite the growth in the adoption of such technology, their reallife use is still questionable. While some users derive longterm value from their trackers, others face barriers to integrate it into their daily routine. Studies have analysed technical aspects of these barriers. In this study, we analyse the behavioural factors of discouragement and wearable abandonment strictly tied to user habits and living circumstances. A data analysis was conducted in two different studies, one with users posts about wearable sales and the other one was a survey analysis. The two studies were used to analyse the stages of wearable adoption, use and abandonment. Therefore, we mainly focused on users motives to get a wearable tracker and to post it for sale. We extracted insights about user motives, highlighted technology condition and limitations, and timeframe before abandonment. The findings revealed certain user behavioural pattern throughout the wearable use and abandonment.
Synthetic Ground Truth Generation for Evaluating Generative Policy Models
Venue: arXiv cs.AI 2019
Year: 2019

Generative Policy-based Models aim to enable a coalition of systems, be they devices or services to adapt according to contextual changes such as environmental factors, user preferences and different tasks whilst adhering to various constraints and regulations as directed by a managing party or the collective vision of the coalition. Recent developments have proposed new architectures to realize the potential of GPMs but as the complexity of systems and their associated requirements increases, there is an emerging requirement to have scenarios and associated datasets to realistically evaluate GPMs with respect to the properties of the operating environment, be it the future battlespace or an autonomous organization. In order to address this requirement, in this paper, we present a method of applying an agile knowledge representation framework to model requirements, both individualistic and collective that enables synthetic generation of ground truth data such that advanced GPMs can be evaluated robustly in complex environments. We also release conceptual models, annotated datasets, as well as means to extend the data generation approach so that similar datasets can be developed for varying complexities and different situations.
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Venue: arXiv cs.LG 2019
Year: 2019

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modeling
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement learning has seen great advancements in the past five years. The successful introduction of deep learning in place of more traditional methods allowed reinforcement learning to scale to very complex domains achieving super-human performance in environments like the game of Go or numerous video games. Despite great successes in multiple domains, these new methods suffer from their own issues that make them often inapplicable to the real world problems. Extreme lack of data efficiency, together with huge variance and difficulty in enforcing safety constraints, is one of the three most prominent issues in the field. Usually, millions of data points sampled from the environment are necessary for these algorithms to converge to acceptable policies. This thesis proposes novel Generative Adversarial Imaginative Reinforcement Learning algorithm. It takes advantage of the recent introduction of highly effective generative adversarial models, and Markov property that underpins reinforcement learning setting, to model dynamics of the real environment within the internal imagination module. Rollouts from the imagination are then used to artificially simulate the real environment in a standard reinforcement learning process to avoid, often expensive and dangerous, trial and error in the real environment. Experimental results show that the proposed algorithm more economically utilises experience from the real environment than the current state-of-the-art Rainbow DQN algorithm, and thus makes an important step towards sample efficient deep reinforcement learning.
Coevo: a collaborative design platform with artificial agents
Venue: arXiv cs.HC 2019
Year: 2019

We present Coevo, an online platform that allows both humans and artificial agents to design shapes that solve different tasks. Our goal is to explore common shared design tools that can be used by humans and artificial agents in a context of creation. This approach can provide a better knowledge transfer and interaction with artificial agents since a common language of design is defined. In this paper, we outline the main components of this platform and discuss the definition of a human-centered language to enhance human-AI collaboration in co-creation scenarios.
Behavioral Program Logic and LAGC Semantics without Continuations (Technical Report)
Venue: arXiv cs.LO 2019
Year: 2019

We present Behavioral Program Logic (BPL), a dynamic logic for trace properties that incorporates concepts from behavioral types and allows reasoning about non-functional properties within a sequent calculus. BPL uses behavioral modalities [s |- τ ], to verify statements s against behavioral specifications τ. Behavioral specifications generalize postconditions and behavioral types. They can be used to specify other static analyses, e.g., data flow analyses. This enables deductive reasoning about the results of multiple analyses on the same program, potentially implemented in different formalisms. Our calculus for BPL verifies the behavioral specification gradually, as common for behavioral types. This vastly simplifies specification, calculus and composition of local results. We present a sequent calculus for object-oriented actors with futures that integrates a pointer analysis and bridges the gap between behavioral types and deductive verification. This technical report introduces (1) complete LAGC semantics of a Core Active Object language (CAO) without continuations (2) Behavioral Program Logic and (3) gives an example for a behavioral type expressed in Behavioral Program Logic, method types. This report contains the soundness proofs for method types. While the semantics cover CAO with suspension, the method types do not, to simplify the presentation.
The Role of User Profile for Fake News Detection
Venue: arXiv cs.SI 2019
Year: 2019

Consuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also enables the widespread of fake news. Because of the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the challenging problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.
Factor Analysis in Fault Diagnostics Using Random Forest
Venue: arXiv cs.LG 2019
Year: 2019

Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes. This type of analysis has been widely used in application such as customer segmentation, medical research, network traffic, image, and video classification. Today, factor analysis is prominently being used in fault diagnosis of machines to identify the significant factors and to study the root cause of a specific machine fault. The advantage of performing factor analysis in machine maintenance is to perform prescriptive analysis (helps answer what actions to take?) and preemptive analysis (helps answer how to eliminate the failure mode?). In this paper, a real case of an industrial rotating machine was considered where vibration and ambient temperature data was collected for monitoring the health of the machine. Gaussian mixture model-based clustering was used to cluster the data into significant groups, and spectrum analysis was used to diagnose each cluster to a specific state of the machine. The significant features that attribute to a particular mode of the machine were identified by using the random forest classification model. The significant features for specific modes of the machine were used to conclude that the clusters generated are distinct and have a unique set of significant features.
Constrained Orthogonal Segment Stabbing
Venue: arXiv cs.CG 2019
Year: 2019

Let $S$ and $D$ each be a set of orthogonal line segments in the plane. A line segment $s\in S$ \emph{stabs} a line segment $s'\in D$ if $s\cap s'\neq\emptyset$. It is known that the problem of stabbing the line segments in $D$ with the minimum number of line segments of $S$ is NP-hard. However, no better than $O(\log |S\cup D|)$-approximation is known for the problem. In this paper, we introduce a constrained version of this problem in which every horizontal line segment of $S\cup D$ intersects a common vertical line. We study several versions of the problem, depending on which line segments are used for stabbing and which line segments must be stabbed. We obtain several NP-hardness and constant approximation results for these versions. Our finding implies, the problem remains NP-hard even under the extra assumption on input, but small constant approximation algorithms can be designed.
Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa. In order to deal with pixel-to-pixel misalignments caused by the pose differences between P(xa) and P(xb), we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. Quantitative and qualitative results, using common datasets and protocols recently proposed for this task, show that our approach is competitive with respect to the state of the art. Moreover, we conduct an extensive evaluation using off-the-shell person re-identification (Re-ID) systems trained with person-generation based augmented data, which is one of the main important applications for this task. Our experiments show that our Deformable GANs can significantly boost the Re-ID accuracy and are even better than data-augmentation methods specifically trained using Re-ID losses.
Reproducible Research is more than Publishing Research Artefacts: A Systematic Analysis of Jupyter Notebooks from Research Articles
Venue: arXiv cs.DL 2019
Year: 2019

With the advent of Open Science, researchers have started to publish their research artefacts (i. e., data, software, and other products of the investigations) in order to allow others to reproduce their investigations. While this publication is beneficial for science in general, it often lacks a comprehensive documentation and completeness with respect to the artefacts. This, in turn, prevents the successful reproduction of the analyses. Typical examples are missing scripts, incomplete datasets or specification of used software. Moreover, issues about licences often create legal concerns. This is true for the use of commercial software but also for the publication of research artefacts without proper sharing licence. As a result, the sole publication of research artefacts does not automatically result in reproducible research. To empirically confirm this, we have been systematically analysing research publications that also published their investigations as Jupyter notebooks. In this paper, we present preliminary results of this analysis for five publications. The results show, that the quality of the published research artefacts must be improved in order to assure reproducibility.
A new object-oriented framework for solving multiphysics problems via combination of different numerical methods
Venue: arXiv cs.MS 2019
Year: 2019

Many interesting phenomena are characterized by the complex interaction of different physical processes, each often best modeled numerically via a specific approach. In this paper, we present the design and implementation of an object-oriented framework for performing multiphysics simulations that allows for the monolithic coupling of different numerical schemes. In contrast, most of the currently available simulation tools are tailored towards a specific numerical model, so that one must resort to coupling different codes externally based on operator splitting. The current framework has been developed following the C++11 standard, and its main aim is to provide an environment that affords enough flexibility for developers to implement complex models while at the same time giving end users a maximum amount of control over finer details of the simulation without having to write additional code. The main challenges towards realizing these objectives are discussed in the paper, together with the manner in which they are addressed. Along with core objects representing the framework skeleton, we present the various polymorphic classes that may be utilized by developers to implement new formulations, material models and solution algorithms. The code architecture is designed to allow achievement of the aforementioned functionalities with a minimum level of inheritance in order to improve the learning curve for programmers who are not acquainted with the software. Key capabilities of the framework are demonstrated via the solution of numerical examples dealing on composite torsion, Biot poroelasticity (featuring a combined finite element-finite volume formulation), and brittle crack propagation using a phase-field approach.
Fair Classification and Social Welfare
Venue: arXiv cs.LG 2019
Year: 2019

Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of social welfare? In this paper, we present a welfare-based analysis of classification and fairness regimes. We translate a loss minimization program into a social welfare maximization problem with a set of implied welfare weights on individuals and groups--weights that can be analyzed from a distribution justice lens. In the converse direction, we ask what the space of possible labelings is for a given dataset and hypothesis class. We provide an algorithm that answers this question with respect to linear hyperplanes in $\mathbb{R}^d$ that runs in $O(n^dd)$. Our main findings on the relationship between fairness criteria and welfare center on sensitivity analyses of fairness-constrained empirical risk minimization programs. We characterize the ranges of $Δε$ perturbations to a fairness parameter $ε$ that yield better, worse, and neutral outcomes in utility for individuals and by extension, groups. We show that applying more strict fairness criteria that are codified as parity constraints, can worsen welfare outcomes for both groups. More generally, always preferring "more fair" classifiers does not abide by the Pareto Principle---a fundamental axiom of social choice theory and welfare economics. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their effectiveness as a means to ensure justice.
Inventory Routing Problem with Facility Location
Venue: arXiv cs.DS 2019
Year: 2019

We study problems that integrate depot location decisions along with the inventory routing problem of serving clients from these depots over time balancing the costs of routing vehicles from the depots with the holding costs of demand delivered before they are due. Since the inventory routing problem is already complex, we study the version that assumes that the daily vehicle routes are direct connections from the depot thus forming stars as solutions, and call this problem the Star Inventory Routing Problem with Facility Location (SIRPFL). As a stepping stone to solving SIRPFL, we first study the Inventory Access Problem (IAP), which is the single depot, single client special case of IRP. The Uncapacitated IAP is known to have a polynomial time dynamic program. We provide an NP-hardness reduction for Capacitated IAP where each demand cannot be split among different trips. We give a $3$-approximation for the case when demands can be split and a $6$-approximation for the unsplittable case. For Uncapacitated SIRPFL, we provide a $12$-approximation by rounding an LP relaxation. Combining the ideas from Capacitated IAP and Uncapacitated SIRPFL, we obtain a $24$-approximation for Capacitated Splittable SIRPFL and a $48$-approximation for the most general version, the Capacitated Unsplittable SIRPFL.
A Style Transfer Approach to Source Separation
Venue: arXiv cs.SD 2019
Year: 2019

Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised source separation depends on the availability of paired mixture-clean training examples. In this paper, we interpret source separation as a style transfer problem. We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains. Using these cycle-consistent variational auto-encoders, we learn a mapping from the mixture domain to the domain of clean sounds and perform source separation without explicitly supervising with paired training examples.